Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
Name
PhysRevD.99.092001.pdf
Description
Published version
Size
6.16 MB
Format
Unknown
Checksum (MD5)
61dc1557179ac5b065c157869b98b5e8
Author(s)
MicroBooNE Collaboration
Ashkenazi, Adi
Carr, Rachel
Collin, G. H.
Conrad, Janet Marie
Diaz, Alejandro
Hen, Or
Hourlier, Adrien C.
Moon, Joongho
Papadopoulou, Afroditi
Date Issued
2019
Journal
Physical Review D
Publisher
American Physical Society (APS)
Version
Final published version
Abstract
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples.
MIT Department
Massachusetts Institute of Technology. Laboratory for Nuclear Science
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PHYSREVD.99.092001
